SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Prostate cancer localization with multispectral MRI based on relevance vector machines
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Wavelet support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Support Vector Machines (SVM) can perform very well on noise free data sets and can usually achieve good classification accuracies when the data is noisy. However, because of the overfitting problem, the accuracy decreases if the SVM is modeled improperly or if the data is excessively noisy or nonlinear. For SVM, most of the misclassification occurs when the test data lies closer to the decision boundary. Therefore in this paper, we investigate the effect of Support Vectors found by SVM, and their effect on the decision when used with the Gaussian kernel. Based on the discussion results we also propose a new technique to improve the performance of SVM by creating smaller clusters along the decision boundary in the higher dimensional space. In this way we reduce the overfitting problem that occurs because of the model selection or the noise effect. As an alternative SVM tuning method, we also propose using only K highest Lagrange multipliers to summarize the decision boundary instead of the whole support vectors and compare the performances. Thus with test results, we show that the number of Support Vectors can be decreased further by using only a fraction of the support vectors found at the training step as a postprocessing method.